This invention relates generally to training machine-learned models, and more particularly to training machine-learned models to predict user responses in sponsored content items.
It is advantageous to generate predictions for instances to improve functionality of an online system. For example, a prediction about whether a user will perform a certain action when presented with a content item can help a system to select the best content items to present to a user to induce desired responses from users in an online system. The predictions are often generated through machine-learned models that predict values for certain characteristics of instances given a set of features extracted from the instances. Often times, the instances are associated with multiple categories. For example, sponsored content items are associated with different content providers that manage the sponsored content items. However, constructing a single machine-learned model that generates predictions for a large number of different categories may result in lack of prediction accuracy due to disregarding differences between individual categories. On the other hand, constructing many machine-learned models for each category may require significant amount of computational resources to maintain the models.